Self-Propelled Robot Path Planning With Multilayer Obstacle Maps
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Solution Overview
Problem
Conventional two-dimensional maps are inadequate for guiding self-propelled robots due to their height differences, leading to low efficiency and poor environment awareness, while three-dimensional maps result in significant computational overhead, prolonging processing time and reducing working efficiency.
Innovation Solution
A self-propelled robot path planning method that acquires information of obstacles at different heights and generates a multilayer environmental map, synthesizing data to plan efficient walking paths by uniting and intersecting two-dimensional maps to identify walkable regions, thereby reducing computational overhead and improving path planning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional maps are used to provide comprehensive spatial information for robot navigation, then environment awareness capability is improved, but computational overhead increases significantly, prolonging data processing time
Solution Approach 1:
The patent segments the three-dimensional environmental map into multiple two-dimensional maps at different height layers. Each two-dimensional map represents obstacle information at a specific height, allowing the robot to process and navigate layer by layer rather than handling the entire 3D map at once, thus reducing computational overhead while maintaining comprehensive environment awareness.
Solution Approach 2:
The patent transforms the three-dimensional map representation into multiple two-dimensional maps by introducing a height layer dimension. Instead of processing a single 3D volumetric map, the system creates stacked 2D maps where each layer corresponds to a specific height range, enabling more efficient processing while preserving vertical obstacle information.
2Productivity
If two-dimensional maps are used for path planning, then computational overhead is reduced and processing speed is improved, but the robot's height differences are not properly accounted for, leading to inadequate navigation guidance
Solution Approach 1:
The patent enhances traditional two-dimensional maps by stacking multiple 2D maps at different height layers. This approach maintains the computational efficiency of 2D processing while adding vertical dimension information through the layering strategy, allowing the robot to account for height differences and navigate around obstacles at various elevations effectively.
3Manufacturing precision
If comprehensive three-dimensional obstacle information is processed, then path planning accuracy is improved, but the complexity of data processing increases, reducing overall working efficiency
Solution Approach 1:
The patent divides complex three-dimensional obstacle information into segmented two-dimensional maps organized by height layers. Each 2D map contains obstacle data for a specific vertical range, simplifying the processing complexity while maintaining comprehensive spatial awareness. The segmentation allows parallel or sequential processing of individual layers rather than handling the entire 3D dataset simultaneously.
Solution Approach 2:
The patent resolves data processing complexity by transforming 3D obstacle information into a stack of 2D representations. This dimensionality change organizes complex spatial data into a more manageable format where vertical information is encoded through layer indexing, reducing the computational burden while preserving path planning accuracy.
Data Source
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AI summary
Provided are a self-propelled robot path planning method, a self-propelled robot and a storage medium. The method may include, a self-propelled robot walks in a to-be-operated space to acquire information of obstacles at different heights and generates a multilayer environmental map of the to-be-operated space. The method may also include information in the multilayer environmental map is synthetically processed to obtain synthetically processed data. Additionally, the method may include a walking path for the self-propelled robot is planned according to the synthetically processed data.